Understand
We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al (2019), in which the generation process is divided into a text-planning stage followed by a plan-realization stage.
- We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than the original planner; (2) we incorporate typing hints that improve the model's ability to deal with unseen relations and entities; (3) we introduce a verification-by-reranking stage that substantially improves the faithfulness of the resulting texts; (4) we incorporate a simple but effective referring expression generation module.
- These extensions result in a generation process that is faster, more fluent, and more accurate.
Built on
Tasnim Mohiuddin and Shafiq Joty. 2019 · 1904
Earlier work this paper cites.
Step-by-step: Separating planning from realization in neural data-to-text generation
Amit Moryossef, Yoav Goldberg, and Ido Dagan. 2019 · 1904
Earlier work this paper cites.
Binary codes capable of correcting deletions, insertions, and reversals
Vladimir I Levenshtein. 1966 · 1966
Earlier work this paper cites.
Building natural language generation systems
Ehud Reiter and Robert Dale. 2000 · 2000
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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